from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
Daal4py_kmeans_short: 0h 0m 1s
Daal4py_ridge: 0h 0m 2s
Kmeans_short: 0h 0m 3s
Daal4py_logisticregression: 0h 0m 4s
Daal4py_kmeans_tall: 0h 0m 8s
Ridge: 0h 0m 11s
Logisticregression: 0h 0m 21s
Kmeans_tall: 0h 0m 25s
Daal4py_kneighborsclassifier_kd_tree: 0h 0m 30s
Kneighborsclassifier_kd_tree: 0h 3m 1s
Daal4py_kneighborsclassifier: 0h 3m 3s
Catboost_symmetric: 0h 5m 9s
Xgboost: 0h 5m 10s
Catboost: 0h 5m 17s
Histgradientboostingclassifier: 0h 5m 31s
Lightgbm: 0h 6m 5s
Kneighborsclassifier: 0h 36m 7s
Total: 1h 11m 17s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config_file_path="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.150 | 0.000 | 5.338 | 0.000 | -1 | 5 | NaN | NaN | 0.531 | 0.000 | 0.282 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 37.806 | 0.000 | 0.000 | 0.038 | -1 | 5 | 0.825 | 0.923 | 2.228 | 0.033 | 16.971 | 0.254 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.183 | 0.018 | 0.000 | 0.183 | -1 | 5 | 1.000 | 1.000 | 0.090 | 0.004 | 2.031 | 0.213 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.140 | 0.000 | 5.728 | 0.000 | 1 | 100 | NaN | NaN | 0.493 | 0.000 | 0.283 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.354 | 0.410 | 0.000 | 0.025 | 1 | 100 | 0.917 | 0.837 | 2.109 | 0.042 | 12.024 | 0.310 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.196 | 0.002 | 0.000 | 0.196 | 1 | 100 | 1.000 | 1.000 | 0.089 | 0.002 | 2.211 | 0.062 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.138 | 0.000 | 5.817 | 0.000 | -1 | 100 | NaN | NaN | 0.502 | 0.000 | 0.274 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 37.176 | 0.000 | 0.000 | 0.037 | -1 | 100 | 0.917 | 0.923 | 2.261 | 0.029 | 16.439 | 0.211 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.176 | 0.018 | 0.000 | 0.176 | -1 | 100 | 1.000 | 1.000 | 0.088 | 0.002 | 1.993 | 0.213 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.135 | 0.000 | 5.946 | 0.000 | 1 | 1 | NaN | NaN | 0.514 | 0.000 | 0.262 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.655 | 0.142 | 0.000 | 0.014 | 1 | 1 | 0.734 | 0.745 | 2.223 | 0.029 | 6.141 | 0.103 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.190 | 0.003 | 0.000 | 0.190 | 1 | 1 | 0.000 | 0.000 | 0.089 | 0.001 | 2.136 | 0.045 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.135 | 0.000 | 5.941 | 0.000 | -1 | 1 | NaN | NaN | 0.519 | 0.000 | 0.259 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.919 | 0.218 | 0.000 | 0.026 | -1 | 1 | 0.734 | 0.745 | 2.213 | 0.034 | 11.715 | 0.207 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.172 | 0.020 | 0.000 | 0.172 | -1 | 1 | 0.000 | 0.000 | 0.092 | 0.003 | 1.875 | 0.231 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.130 | 0.000 | 6.152 | 0.000 | 1 | 5 | NaN | NaN | 0.531 | 0.000 | 0.245 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.128 | 0.129 | 0.000 | 0.025 | 1 | 5 | 0.825 | 0.837 | 2.186 | 0.049 | 11.495 | 0.265 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.197 | 0.002 | 0.000 | 0.197 | 1 | 5 | 1.000 | 1.000 | 0.089 | 0.003 | 2.203 | 0.089 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.064 | 0.000 | 0.250 | 0.000 | -1 | 5 | NaN | NaN | 0.117 | 0.000 | 0.547 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 33.932 | 0.000 | 0.000 | 0.034 | -1 | 5 | 0.985 | 0.986 | 0.392 | 0.002 | 86.572 | 0.549 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.027 | 0.001 | 0.000 | 0.027 | -1 | 5 | 1.000 | 1.000 | 0.007 | 0.001 | 3.914 | 0.380 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.076 | 0.000 | 0.212 | 0.000 | 1 | 100 | NaN | NaN | 0.115 | 0.000 | 0.659 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.343 | 0.104 | 0.000 | 0.021 | 1 | 100 | 0.984 | 0.986 | 0.341 | 0.015 | 62.629 | 2.708 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.001 | 0.000 | 0.020 | 1 | 100 | 1.000 | 1.000 | 0.007 | 0.001 | 2.924 | 0.318 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.062 | 0.000 | 0.257 | 0.000 | -1 | 100 | NaN | NaN | 0.119 | 0.000 | 0.523 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 33.775 | 0.000 | 0.000 | 0.034 | -1 | 100 | 0.984 | 0.986 | 0.411 | 0.011 | 82.267 | 2.170 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.028 | 0.004 | 0.000 | 0.028 | -1 | 100 | 1.000 | 1.000 | 0.007 | 0.000 | 4.339 | 0.643 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.087 | 0.000 | 0.184 | 0.000 | 1 | 1 | NaN | NaN | 0.122 | 0.000 | 0.711 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 11.014 | 0.358 | 0.000 | 0.011 | 1 | 1 | 0.973 | 0.976 | 0.347 | 0.009 | 31.750 | 1.341 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.017 | 0.002 | 0.000 | 0.017 | 1 | 1 | 1.000 | 1.000 | 0.007 | 0.000 | 2.601 | 0.325 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.069 | 0.000 | 0.232 | 0.000 | -1 | 1 | NaN | NaN | 0.120 | 0.000 | 0.574 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 23.321 | 0.403 | 0.000 | 0.023 | -1 | 1 | 0.973 | 0.976 | 0.341 | 0.008 | 68.405 | 1.924 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.001 | 0.000 | 0.021 | -1 | 1 | 1.000 | 1.000 | 0.007 | 0.001 | 3.151 | 0.519 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.067 | 0.000 | 0.238 | 0.000 | 1 | 5 | NaN | NaN | 0.121 | 0.000 | 0.556 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.389 | 0.065 | 0.000 | 0.021 | 1 | 5 | 0.985 | 0.986 | 0.339 | 0.004 | 63.008 | 0.856 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.001 | 0.000 | 0.021 | 1 | 5 | 1.000 | 1.000 | 0.007 | 0.001 | 2.934 | 0.462 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.495 | 0.000 | 0.023 | 0.000 | -1 | 1 | NaN | NaN | 0.823 | 0.000 | 4.246 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.516 | 0.009 | 0.000 | 0.001 | -1 | 1 | 0.943 | 0.982 | 0.198 | 0.006 | 2.601 | 0.085 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.002 | 0.000 | 0.004 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 8.998 | 5.898 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.525 | 0.000 | 0.023 | 0.000 | 1 | 100 | NaN | NaN | 0.868 | 0.000 | 4.064 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 5.200 | 0.099 | 0.000 | 0.005 | 1 | 100 | 0.970 | 0.986 | 0.609 | 0.010 | 8.539 | 0.214 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | 1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 3.568 | 1.378 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.442 | 0.000 | 0.023 | 0.000 | 1 | 1 | NaN | NaN | 0.807 | 0.000 | 4.265 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.825 | 0.017 | 0.000 | 0.001 | 1 | 1 | 0.943 | 0.982 | 0.200 | 0.005 | 4.124 | 0.135 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 2.468 | 1.025 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.278 | 0.000 | 0.024 | 0.000 | -1 | 100 | NaN | NaN | 0.825 | 0.000 | 3.975 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 3.170 | 0.158 | 0.000 | 0.003 | -1 | 100 | 0.970 | 0.966 | 0.107 | 0.002 | 29.657 | 1.578 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.007 | 0.001 | 0.000 | 0.007 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 22.150 | 10.362 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.432 | 0.000 | 0.023 | 0.000 | -1 | 5 | NaN | NaN | 0.812 | 0.000 | 4.224 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.952 | 0.012 | 0.000 | 0.001 | -1 | 5 | 0.971 | 0.986 | 0.586 | 0.008 | 1.624 | 0.029 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.002 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 3.828 | 2.144 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.546 | 0.000 | 0.023 | 0.000 | 1 | 5 | NaN | NaN | 0.784 | 0.000 | 4.523 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.635 | 0.021 | 0.000 | 0.002 | 1 | 5 | 0.971 | 0.966 | 0.105 | 0.002 | 15.549 | 0.391 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.198 | 2.777 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.944 | 0.000 | 0.017 | 0.000 | -1 | 1 | NaN | NaN | 0.543 | 0.000 | 1.737 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.032 | 0.002 | 0.001 | 0.000 | -1 | 1 | 0.973 | 0.980 | 0.001 | 0.000 | 24.996 | 8.644 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 17.745 | 14.001 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.935 | 0.000 | 0.017 | 0.000 | 1 | 100 | NaN | NaN | 0.543 | 0.000 | 1.722 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.065 | 0.004 | 0.000 | 0.000 | 1 | 100 | 0.984 | 0.983 | 0.008 | 0.001 | 7.770 | 1.398 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 4.696 | 2.799 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 1.020 | 0.000 | 0.016 | 0.000 | 1 | 1 | NaN | NaN | 0.562 | 0.000 | 1.815 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.030 | 0.004 | 0.001 | 0.000 | 1 | 1 | 0.973 | 0.980 | 0.002 | 0.000 | 19.780 | 6.863 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.370 | 3.658 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 1.001 | 0.000 | 0.016 | 0.000 | -1 | 100 | NaN | NaN | 0.543 | 0.000 | 1.844 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.057 | 0.005 | 0.000 | 0.000 | -1 | 100 | 0.984 | 0.969 | 0.001 | 0.000 | 55.074 | 17.298 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 16.398 | 13.094 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.973 | 0.000 | 0.016 | 0.000 | -1 | 5 | NaN | NaN | 0.554 | 0.000 | 1.756 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.034 | 0.003 | 0.000 | 0.000 | -1 | 5 | 0.981 | 0.983 | 0.009 | 0.002 | 3.874 | 0.765 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 22.193 | 14.213 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.968 | 0.000 | 0.017 | 0.000 | 1 | 5 | NaN | NaN | 0.550 | 0.000 | 1.760 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.034 | 0.002 | 0.000 | 0.000 | 1 | 5 | 0.981 | 0.969 | 0.001 | 0.000 | 37.333 | 14.751 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 6.362 | 5.422 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.756 | 0.000 | 0.635 | 0.000 | random | NaN | 30 | NaN | 0.484 | 0.0 | 1.564 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.268 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 11.007 | 6.302 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.001 | 0.000 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 13.238 | 9.468 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.706 | 0.000 | 0.680 | 0.000 | k-means++ | NaN | 30 | NaN | 0.508 | 0.0 | 1.390 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.295 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 10.174 | 6.027 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.002 | 0.000 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 16.916 | 16.508 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.971 | 0.000 | 3.443 | 0.000 | random | NaN | 30 | NaN | 2.968 | 0.0 | 2.349 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.001 | 10.292 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 7.839 | 4.502 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.001 | 0.013 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 11.953 | 7.729 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.735 | 0.000 | 3.563 | 0.000 | k-means++ | NaN | 30 | NaN | 3.149 | 0.0 | 2.139 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 12.732 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 6.440 | 2.764 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.016 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.768 | 6.203 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.275 | 0.000 | 0.012 | 0.000 | k-means++ | NaN | 20 | NaN | 0.109 | 0.0 | 2.531 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.151 | 0.000 | k-means++ | -0.000 | 20 | -0.000 | 0.001 | 0.0 | 2.704 | 0.667 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.001 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.263 | 6.122 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.092 | 0.000 | 0.035 | 0.000 | random | NaN | 20 | NaN | 0.037 | 0.0 | 2.514 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.156 | 0.000 | random | -0.001 | 20 | 0.000 | 0.001 | 0.0 | 2.721 | 0.410 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.000 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.876 | 5.447 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.724 | 0.000 | 0.221 | 0.000 | k-means++ | NaN | 20 | NaN | 0.412 | 0.0 | 1.759 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 5.453 | 0.000 | k-means++ | 0.278 | 20 | 0.319 | 0.001 | 0.0 | 2.053 | 0.344 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.010 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.574 | 4.113 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.253 | 0.000 | 0.633 | 0.000 | random | NaN | 20 | NaN | 0.166 | 0.0 | 1.524 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 5.313 | 0.000 | random | 0.320 | 20 | 0.373 | 0.001 | 0.0 | 2.101 | 0.442 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.010 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.657 | 4.446 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 12.228 | 0.0 | [-0.09648522] | 0.000 | NaN | NaN | NaN | NaN | NaN | 2.079 | 0.0 | 5.883 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.001 | 0.0 | [31.6472972] | 0.000 | NaN | NaN | NaN | NaN | 0.506 | 0.000 | 0.0 | 1.265 | 1.180 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.20765224] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.0 | 0.384 | 0.306 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.850 | 0.0 | [2.44734793] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.792 | 0.0 | 1.074 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [109.46005355] | 0.000 | NaN | NaN | NaN | NaN | 0.240 | 0.003 | 0.0 | 0.562 | 0.081 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [19.65185975] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.136 | 0.092 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.201 | 0.000 | 0.399 | 0.0 | NaN | NaN | NaN | 0.200 | 0.0 | 1.005 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.001 | 7.660 | 0.0 | NaN | NaN | 0.094 | 0.017 | 0.0 | 0.619 | 0.036 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 1.062 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.658 | 0.636 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.610 | 0.000 | 0.497 | 0.0 | NaN | NaN | NaN | 0.266 | 0.0 | 6.046 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 5.214 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.601 | 0.391 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.012 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.621 | 0.600 | See | See |
reporting_hpo = ReportingHpo(files=[
"results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
"results/benchmarking/xgboost_XGBClassifier.csv",
"results/benchmarking/lightgbm_LGBMClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier_symmetric.csv",
])
reporting_hpo.run()